url <- 'https://raw.githubusercontent.com/kyleknox3/STA553/main/week6/LifeIncomePopulation.csv'
data <- read.csv(url)
income <- na.omit(data)
data2015 <- income %>%
filter(year == 2015)
write.csv(data2015, "/Users/kyleknox/Documents/M.S. Applied Statistics/SPRING 2024/STA533/week6/data2015.csv", row.names = FALSE)
regions <- c("Asia" = "#332288", "Europe" = "#117733", "Africa" = "#44AA99", "Americas" = "#88CCEE", "Oceania" = "#AA4499")
gg <- ggplot(data2015, aes(x = lifeExp, y = income, size = population,
color = region, text = paste("Country:", country,"<br>Population:", population, "<br>Income:", income))) +
geom_point(alpha = 0.7) +
scale_size_continuous(range = c(1, 20)) +
theme_minimal() +
labs(title = "Life Expectancy vs. Income in 2015",
x = "Life Expectancy",
y = "Income") +
scale_color_manual(values = regions, name = "Region") +
geom_point(shape = 21, colour = "black", fill = NA)
plotly_gg <- ggplotly(gg, tooltip = "text")
plotly_gg
A Look Inside 2015
In the year 2015, there is a positive correlation between life
expectancy and income. The higher the average income for a country, the
better access individuals have to healthcare, nutrition, and living
conditions which directly impact life expectancy. The varying income
across the regions of the world show how economic difficulties influence
life expectancy. Countries in more developed regions have higher income
and longer life expectancy compared to those of the underdeveloped
world. However, when looking at population, the countries with larger
populations could have a lower average due to resource allocation and
the wealth gap. Therefore, looking at larger countries median income may
be more beneficial. Overall, there is a general global trend towards
higher life expectancy and income, signifying worldwide improvements in
healthcare, technology, and economic development. Although, it is
apparent that these improvements are not evenly distributed across the
all regions of the world.
url <- 'https://raw.githubusercontent.com/kyleknox3/STA553/main/week6/LifeIncomePopulation.csv'
data <- read.csv(url)
income2 <- na.omit(data)
regions <- c("Asia" = "#332288", "Europe" = "#117733", "Africa" = "#44AA99", "Americas" = "#88CCEE", "Oceania" = "#AA4499")
plot <- ggplot(income2, aes(x = lifeExp, y = income, size = population, color = region)) +
geom_point(alpha = 0.7, show.legend = TRUE, aes(group = year)) +
scale_size(range = c(2, 12), name = "Population Size") +
scale_color_manual(values = regions, name = "Region") +
labs(title= 'Life Expectancy and Income Over the Years', subtitle = 'Year: {frame_time}', x = 'Life Expectancy', y = 'Income') +
theme_minimal() +
transition_time(year) +
ease_aes('linear')
animate(plot, width = 900, height = 500, duration = 30)
A Look Inside the Income and Life Expextancy
Across the World
There has been a significant increase in the average life expectancy
globally since the early 1800s. The shift is more prevalent in the early
20th century. These life expectancy improvements could be related to
advancements in healthcare, nutrition, and living conditions over time.
Income has an overall positive trend, with a steep incline
after the mid 20th century. Some causes for these increases could be a
result of industrialization, globalization, and technological advances
that have influenced productivity. There is a significant increase in
income in the late 20th century, which could be indicative of economic
expansion.
The worlds population has also shown significant
growth since the 1800s, with an acceleration in the rate of increase
from the 20th century to present day. This increased population could be
a result of decreased mortality rates, medical advancements, and other
factors leading to higher birth rates and lower death rates. With a
continued increase in the global population challenges like resource
management, sustainability, and a detrimental environmental impact will
arise.
With the increased income, population, and life
expectancy over the past few centuries, it shows the the global economic
increases for the worlds population. However, these increases do provide
challenges for the future in sustainability and the distribution of
resources. These challenges are not just limited regionally but also
globally.
---
title: "Life Expectancy and Income for the Year 2015"
author: "Kyle Knox"
date: "Week 6"
output:
  html_document: 
    toc: yes
    toc_depth: 4
    toc_float: yes
    fig_width: 6
    number_sections: yes
    toc_collapsed: yes
    code_folding: hide
    code_download: yes
    smooth_scroll: true
    theme: readable
    fig_height: 4
---
  
  <style type="text/css">
  
  div#TOC li {
list-style:none;
background-color:lightgray;
background-image:none;
background-repeat:none;
background-position:0;
font-family: Arial, Helvetica, sans-serif;
color: #780c0c;
  }

/* mouse over link */
  div#TOC a:hover {
color: red;
}

/* unvisited link */
  div#TOC a:link {
color: blue;
}



h1.title {
  font-size: 24px;
  color: Darkblue;
  text-align: center;
  font-family: Arial, Helvetica, sans-serif;
  font-variant-caps: normal;
}
h4.author { 
  font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkRed;
  text-align: center;
}
h4.date { 
  font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
  text-align: center;
}
h1 {
  font-size: 22px;
  font-family: "Times New Roman", Times, serif;
  color: darkred;
  text-align: center;
}
h2 { /
    font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: navy;
  text-align: left;
}

h3 { 
  font-size: 15px;
  font-family: "Times New Roman", Times, serif;
  color: navy;
  text-align: left;
}

h4 { /* Header 4 - and the author and data headers use this too  */
    font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: darkred;
  text-align: left;
}

/* unvisited link */
  a:link {
    color: green;
  }

/* visited link */
  a:visited {
    color: green;
  }

/* mouse over link */
  a:hover {
    color: red;
  }

/* selected link */
  a:active {
    color: yellow;
  }

</style>
  
  
  ```{r setup, include=FALSE}
# code chunk specifies whether the R code, warnings, and output 
# will be included in the output files.
options(repos = list(CRAN="http://cran.rstudio.com/"))
if (!require("tidyverse")) {
  install.packages("tidyverse")
  library(tidyverse)
}
if (!require("knitr")) {
  install.packages("knitr")
  library(knitr)
}
if (!require("cowplot")) {
  install.packages("cowplot")
  library(cowplot)
}
if (!require("latex2exp")) {
  install.packages("latex2exp")
  library(latex2exp)
}
if (!require("plotly")) {
  install.packages("plotly")
  library(plotly)
}
if (!require("gapminder")) {
  install.packages("gapminder")
  library(gapminder)
}
if (!require("png")) {
  install.packages("png")             # Install png package
  library("png")
}
if (!require("RCurl")) {
  install.packages("RCurl")             # Install RCurl package
  library("RCurl")
}
if (!require("colourpicker")) {
  install.packages("colourpicker")              
  library("colourpicker")
}
if (!require("gifski")) {
  install.packages("gifski")              
  library("gifski")
}
if (!require("magick")) {
  install.packages("magick")              
  library("magick")
}
if (!require("grDevices")) {
  install.packages("grDevices")              
  library("grDevices")
}
### ggplot and extensions
if (!require("ggplot2")) {
  install.packages("ggplot2")              
  library("ggplot2")
}
if (!require("gganimate")) {
  install.packages("gganimate")              
  library("gganimate")
}
if (!require("ggridges")) {
  install.packages("ggridges")              
  library("ggridges")
}
if (!require("dplyr")) {
  install.packages("dplyr")              
  library("dplyr")
}
if (!require("htmltools")) {
  install.packages("htmltools")              
  library("htmltools")
}
if (!require("rmarkdown")) {
  install.packages("rmarkdown")              
  library("rmarkdown")
}
# knitr::opts_knit$set(root.dir = "/Users/kyleknox/Documents/M.S. Applied Statistics/SPRING 2024/STA533/week6)
# knitr::opts_knit$set(root.dir = "C:\\STA490\\w05")

knitr::opts_chunk$set(echo = TRUE,       
                      warning = FALSE,   
                      result = TRUE,   
                      message = FALSE)
```
```{r code, fig.width=10, fig.height=6.8}
url <- 'https://raw.githubusercontent.com/kyleknox3/STA553/main/week6/LifeIncomePopulation.csv'
data <- read.csv(url)
income <- na.omit(data)

data2015 <- income %>%
  filter(year == 2015)

write.csv(data2015, "/Users/kyleknox/Documents/M.S. Applied Statistics/SPRING 2024/STA533/week6/data2015.csv", row.names = FALSE)

regions <- c("Asia" = "#332288", "Europe" = "#117733", "Africa" = "#44AA99", "Americas" = "#88CCEE", "Oceania" = "#AA4499")

gg <- ggplot(data2015, aes(x = lifeExp, y = income, size = population, 
      color = region, text = paste("Country:", country,"<br>Population:", population, "<br>Income:", income))) +
  geom_point(alpha = 0.7) + 
  scale_size_continuous(range = c(1, 20)) + 
  theme_minimal() +
  labs(title = "Life Expectancy vs. Income in 2015",
       x = "Life Expectancy",
       y = "Income") +
  scale_color_manual(values = regions, name = "Region") + 
  geom_point(shape = 21, colour = "black", fill = NA) 


plotly_gg <- ggplotly(gg, tooltip = "text")

plotly_gg

```

<br>
<br>
<div style="text-align: center;"><span style="color: red;">A Look Inside 2015</span></div>

<div style="width: 95%; margin: 0 auto; text-align: justify; border: 1px solid #ccc; padding: 10px; box-sizing: border-box;">
  <p style="text-align: justify;">
In the year 2015, there is a positive correlation between life expectancy and income. The higher the average income for a country, the better access individuals have to healthcare, nutrition, and living conditions which directly impact life expectancy. The varying income across the regions of the world show how economic difficulties influence life expectancy.  Countries in more developed regions have higher income and longer life expectancy compared to those of the underdeveloped world. However, when looking at population, the countries with larger populations could have a lower average due to resource allocation and the wealth gap. Therefore, looking at larger countries median income may be more beneficial. Overall, there is a general global trend towards higher life expectancy and income, signifying worldwide improvements in healthcare, technology, and economic development.  Although, it is apparent that these improvements are not evenly distributed across the all regions of the world. <br> 
 </p>
</div>
<br>
<br>

```{r plot-animation, fig.width=10, fig.height=6.8}
url <- 'https://raw.githubusercontent.com/kyleknox3/STA553/main/week6/LifeIncomePopulation.csv'
data <- read.csv(url)
income2 <- na.omit(data)

regions <- c("Asia" = "#332288", "Europe" = "#117733", "Africa" = "#44AA99", "Americas" = "#88CCEE", "Oceania" = "#AA4499")

plot <- ggplot(income2, aes(x = lifeExp, y = income, size = population, color = region)) +
  geom_point(alpha = 0.7, show.legend = TRUE, aes(group = year)) +
  scale_size(range = c(2, 12), name = "Population Size") +
  scale_color_manual(values = regions, name = "Region") +
  labs(title= 'Life Expectancy and Income Over the Years', subtitle = 'Year: {frame_time}', x = 'Life Expectancy', y = 'Income') +
  theme_minimal() +
  transition_time(year) +
  ease_aes('linear')

animate(plot, width = 900, height = 500, duration = 30)
```
<br>
<br>
<div style="text-align: center;"><span style="color: red;">A Look Inside the Income and Life Expextancy Across the World</span></div>

<div style="width: 95%; margin: 0 auto; text-align: justify; border: 1px solid #ccc; padding: 10px; box-sizing: border-box;">
  <p style="text-align: justify;">
There has been a significant increase in the average life expectancy globally since the early 1800s.  The shift is more prevalent in the early 20th century.  These life expectancy improvements could be related to advancements in healthcare, nutrition, and living conditions over time. <br>
<br>
Income has an overall positive trend, with a steep incline after the mid 20th century.  Some causes for these increases could be a result of industrialization, globalization, and technological advances that have influenced productivity. There is a significant increase in income in the late 20th century, which could be indicative of economic expansion. <br>
<br>
The worlds population has also shown significant growth since the 1800s, with an acceleration in the rate of increase from the 20th century to present day.  This increased population could be a result of decreased mortality rates, medical advancements, and other factors leading to higher birth rates and lower death rates. With a continued increase in the global population challenges like resource management, sustainability, and a detrimental environmental impact will arise.<br>
<br>
With the increased income, population, and life expectancy over the past few centuries, it shows the the global economic increases for the worlds population.  However, these increases do provide challenges for the future in sustainability and the distribution of resources. These challenges are not just limited regionally but also globally.<br>
 </p>
</div>
<br>
<br>

